Experience Structuring Factors Affecting Learning in Family Visits to Museums

Hatala, Marek; Tanenbaum, Karen; Wakkary, Ron; Muise, Kevin; Mohabbati, Bardia; Corness, Greg; Budd, Jillian; Loughin, Tom

doi:10.1007/978-3-642-04636-0_6

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…• User Satisfaction: The user's satisfaction with the system was measured in [178] and [200], as well as the user's perceived usefulness of the recommendations in [201]. The benefits of the system with respect to learning and enjoyment were also measured in [177], and the ease of use for both students and teachers in [88]. Evaluation tools used were: the Technology Acceptance Model (TAM) [261] in [88]; the System Usability Scale (SUS) [262] in [6] and [64]; the General Interest Structure Test (AIST) [263] in [180]; interviews in [178]; observations in [179] and [200]; recording participants' interactions and collecting think aloud protocols in [5].…”

Section: Effects Measured By Evaluationmentioning

confidence: 99%

“…Evaluation tools used were: the Technology Acceptance Model (TAM) [261] in [88]; the System Usability Scale (SUS) [262] in [6] and [64]; the General Interest Structure Test (AIST) [263] in [180]; interviews in [178]; observations in [179] and [200]; recording participants' interactions and collecting think aloud protocols in [5]. • Task Support: Learners were supported in achieving their current learning goals in [170] and [177], as well as to accomplish their learning or authoring tasks in [5]. The effectiveness and applicability of the recommendations in supporting learning tasks were also measured in [82].…”

Section: Effects Measured By Evaluationmentioning

confidence: 99%

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Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Erdt

Fernández

Rensing

2015

IEEE Trans. Learning Technol.

118

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The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.

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Section: Effects Measured By Evaluationmentioning

confidence: 99%

Section: Effects Measured By Evaluationmentioning

confidence: 99%

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Erdt

Fernández

Rensing

2015

IEEE Trans. Learning Technol.

118

View full text Add to dashboard Cite

show abstract

“…The reasoning engine that drives the guidance system on the tabletop (described below) thus functions essentially as a knowledgebased recommender, helping the "reader" move through the story in a coherent manner. As a result, the intelligence techniques used in the system are most similar to those used in recommender systems in educational and informational applications, where the goal is to present a static body of content to the user in an intelligent and dynamic manner based on her choices and actions (Hatala and Wakkary 2005;Damiano et al 2008;Hatala et al 2009). …”

Section: Interactive Storytellingmentioning

confidence: 99%

A case study of intended versus actual experience of adaptivity in a tangible storytelling system

Tanenbaum

Hatala

Tanenbaum

et al. 2013

User Model User-Adap Inter

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This article presents a case study of an adaptive, tangible storytelling system called "The Reading Glove". The research addresses a gap in the field of adaptivity for ubiquitous systems by taking a critical look at the notion of "adaptivity" and how users experience it. The Reading Glove is an interactive storytelling system featuring a wearable, glove-based interface and a set of narratively rich objects. A tabletop display provides adaptive recommendations which highlight objects to select next, functioning as an expert storytelling system. The recommendation engine can be run in three different configurations to examine the effects of different adaptive methods. The study of the design process as well as the user experience of the Reading Glove allows us to develop a deeper understanding of the experience of adaptivity that is useful for designers of intelligent systems, particularly those with ubiquitous and tangible forms of interaction.

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“…One other source of learning is to utilize the museum Hatala et al, (2009). Because in the use of museums that are used as a source of history learning can increase students' learning motivation, thus supporting the achievement of competencies, and supporting teaching programs that involve research activities in the field of history study.…”

Section: Utilization Of the Lampung Museum As A Learning Resource His...mentioning

confidence: 99%

Utilization of the Lampung Museum as a Learning Resource of Historical Learning at Senior High School

Fadli

Acim

2022

pedagogia

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This study aims: (1) to know Lampung museum collections that can be used as a learning resource historical learning in Metro 2 State Senior High School; (2) Utilization of the Lampung museum as a learning resource historical learning in Metro 2 State Senior High School; (3) Constraints faced in the use of the Lampung museum as a learning resource historical learning. The research method used is the qualitative descriptive analysis of Milles and Hubermans models. Data collection techniques used were observation, in-depth interviews, and documentation. Data analysis is data reduction, data display, and data verification. The results showed that the Lampung museum collection was divided into 10 categories, namely geology, biology, ethnography, archeology, history, numismatics, philology, ceramics, fine arts, and technology, from all the collections can be used as a learning resource historical learning. The utilization of the Lampung museum as a learning resource historical learning has been carried out but not fully maximized. Lampung museum collections are used as a source of learning history by adjusting to the Competency Standards and Basic Competence curriculum for historical subjects. The implementation of history learning makes the Lampung museum as a source of learning done by the method of study visits.

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Experience Structuring Factors Affecting Learning in Family Visits to Museums

Cited by 5 publications

References 11 publications

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

A case study of intended versus actual experience of adaptivity in a tangible storytelling system

Utilization of the Lampung Museum as a Learning Resource of Historical Learning at Senior High School

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